DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a non-final rejection is in response to Applicant’s amendment of 04 August 2025. Claims 1-20 are currently pending, as discussed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05 November 2025 has been entered.
Response to Arguments
Applicant's arguments filed 04 August 2025 have been fully considered and are not persuasive. Amendments to claims 1, 8 and 14 have been fully considered and 35 U.S.C. § 112(a), 112(b), and 101 rejection is sustained.
Examiner has carefully considered Applicant’s arguments regarding 35 U.S.C. § 103 rejections and agrees that Penilla in view of Precision Tune Autocare does not teach the amended features of claim 1, 8 and 14. Examiner agrees with applicant arguments and withdraws the 35 U.S.C. 103 rejection for claims 1-20 set forth in office action of 23 July 2025 but is moot in view of new obviousness rejection necessitated by the amendments.
Examiner’s Response- Examiner has carefully considered Applicant’s arguments and respectfully disagrees. Applicant has amended the independent claims to include limitations: adjusting an operational parameter of the subsystem to maintain safe operating conditions and limiting operation of the vehicle to reduce operational stress on the subsystem. Supporting paragraphs [0040] and [0034] do not describe any operational parameter adjustments by the computer in particular the quote: “In this case where the engine temperature range is exceeded, it is important to take immediate action to address the issue to prevent engine damage and potential safety hazards (e.g., engine fire)” [¶34] no active steps are adjustments are described to maintain safe operating conditions. The disclosure only describes detection of a problem and the automatic action in response to detection of a problem is scheduling an appointment at a repair shop [¶40]. Supporting paragraphs [0041], [0034], and [0053] do not support the claim that the computer automatically limits operation of the vehicle to reduce stress on the subsystem. For example, scheduling an appointment for maintenance is not the same as limiting operation of the vehicle, an appointment is a calendar event and does not suggest any limits on operation of the vehicle. Example stated in ¶34 does not describe any limits on operation of the vehicle and only expresses that an action must be done to address the issue, but does not describe what that action is and only that it is important to do said action. Since no actions are disclosed, Examiner cannot determine how to interpret the claim as it has not been described in the claim language or in specification. Example discussed in ¶53 only describes further automatically scheduling a maintenance appointment but does not support any claimed limits on operation of the vehicle.
Examiner disagrees that the amended claims overcome the 101 rejection because the recited limitations that would integrate an abstract idea into practical application, is not disclosed by the specification. Furthermore, the argument that Step 1: the claim is not directed to an abstract idea is not persuasive because the argument that solving a technical problem uses abstract ideas that can be performed in the human mind. For example, a person driving a vehicle can determine that based on how many miles they have driven the car, that they are due for an oil change and schedule, based on that assessment schedule a maintenance appointment by calling a repair shop and using their voice to ask for an appointment. Utilizing machine learning models are further directed to an abstract idea of mathematics and therefore cannot be considered and additional element. In response to Step 2: the additional elements do not bring the abstract ideas into practical application since the additional elements are mere data gathering using IoT sensors and sending notifications. Updating machine learning models further recites an improvement to a machine learning model which is directed toward an abstract idea (mathematics) and therefore cannot be considered additional elements and therefore do not add significantly more to overcome the 101 rejections.
Additionally, regarding claim 14, A computer program product is further rejected under 101 because the claimed invention is directed to non-statutory subject matter, see MPEP section 2106.01. A computer program product does note cite a non-transitory term, so it could be interpreted as a carrier wave. It is suggested that the applicant amend claim 14 to include the term “non-transitory”. Dependent claims 15-20 are also rejected under 101 because they do not further provide statutory subject matter to the claims.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 11/13/2025 and 12/15/2025 has been considered by examiner.
Claim interpretation
Examiner interprets the computer program product of claim 14 as not to include transitory signals per se, per filed paragraph 10 of the specification.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20, are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claims 4, 12 and 20, the independent claims recite “adjusting an operational parameter of the subsystem to maintain safe operating conditions and limiting operation of the vehicle to reduce operational stress on the subsystem” which is not explicitly, implicitly or inherently disclosed in the Specification. In this respect, the examiner looks to the following specification passages in order to find, if possible, support in sufficient detail:
Supporting paragraphs [0040] and [0034] do not describe any operational parameter adjustments by the computer in particular the quote: “In this case where the engine temperature range is exceeded, it is important to take immediate action to address the issue to prevent engine damage and potential safety hazards (e.g., engine fire)” [¶34] no active steps are adjustments are described to maintain safe operating conditions. The disclosure only describes detection of a problem and the automatic action in response to detection of a problem is scheduling an appointment at a repair shop [¶40]. Supporting paragraphs [0041], [0034], and [0053] do not support the claim that the computer automatically limits operation of the vehicle to reduce stress on the subsystem. For example, scheduling an appointment for maintenance is not the same as limiting operation of the vehicle, an appointment is a calendar event and does not suggest any limits on operation of the vehicle. Example stated in ¶34 does not describe any limits on operation of the vehicle and only expresses that an action must be done to address the issue, but does not describe what that action is and only that it is important to do said action. Since no actions are disclosed, Examiner cannot determine how to interpret the claim as it has not been described in the claim language or in specification. Example discussed in ¶53 only describes further automatically scheduling a maintenance appointment but does not support any claimed limits on operation of the vehicle.
Accordingly, the Examiner believes that applicant has not demonstrated to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8 and 14 are indefinite because Examiner does not know how to interpret the limitations “adjusting an operational parameter of the subsystem to maintain safe operating conditions and limiting operation of the vehicle to reduce operational stress on the subsystem” since it has not been disclosed in the specification.
Claims 2-7, 9-13, and 15-20 are rejected as being dependent on a rejected claim.
Claim(s) depending from claims expressly noted above are also rejected under 35 U.S.C. 112 by/for reason of their dependency from a noted claim that is rejected under 35 U.S.C. 112, for the reasons given.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more and Claim 14 is directed to non-statutory subject matter.
101 Analysis – Step 1 – YES
Claim 1 is directed to a method and claim 8 is directed toward a system. Therefore, claims 1, 8 are within at least one of the four statutory categories.
Claim 14 is directed to a computer program product which is not directed to statutory subject matter, see MPEP section 2106.01. A computer program product does not cite a “on-transitory term, so it could be interpreted as a carrier wave. It is suggested that the applicant amend claim 14 to include the term “non-transitory”. Dependent claims 15-20 are also rejected under 101 because they do not further provide statutory subject matter to the claims.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 include limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claims 8 and 14 are analogous to each other so claim 8 and 14 is rejected for the same reasons as the representative claim 1 as discussed here.
Claim 1 recites:
A computer-implemented method for dynamic vehicle maintenance management, the computer-implemented method comprising:
collecting, by a computer, data from an Internet of Things (IoT) sensor system onboard a vehicle;
analyzing, utilizing a set of machine learning models trained on historical and real- time data, the collected data to assess driving behavior of a user of the vehicle combined with identification of an issue with a subsystem of the vehicle;
determining, by the computer, a customized maintenance schedule for the vehicle that takes into account the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle;
predicting, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle;
taking preventative action, by the computer, to avoid the potential subsystem failure, wherein the preventative action comprises at least one of:
adjusting an operational parameter of the subsystem to maintain safe operating conditions and limiting operation of the vehicle to reduce operational stress on the subsystem;
scheduling, by the computer, a maintenance appointment with a vehicle repair shop computer, wherein the maintenance appointment corresponds to the identification of the issue with the subsystem of the vehicle at a date, time, and location based on availability of the user of the vehicle and a selected vehicle repair shop;
sending, by the computer, a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle;
collecting, by the computer, user feedback regarding the maintenance appointment;
and updating the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time and provide proactive detection by addressing maintenance issues to prevent vehicle breakdown and accidents caused by failure of one or more vehicle subsystems.
The examiner submits that the foregoing bolded limitation(s) constitute “mathematical concepts” and “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, analyzing, utilizing a set of machine learning models trained on historical and real- time data, the collected data to assess driving behavior of a user of the vehicle combined with identification of an issue with a subsystem of the vehicle; in the context of this claim encompasses using a mathematical concept (trained machine learning model) to perform a mental process: a person looking at data collected (received, detected, based on data from a sensor, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) about the driving behavior of the user and identify an issue with a vehicle subsystem either mentally or using a pen and paper.
Examiner notes that MPEP 2106.04(a)(2)(III): The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Here, the determination is a form of making evaluation and judgement based on observation (driver behavior).
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
Claim 1 recites:
A computer-implemented method for dynamic vehicle maintenance management, the computer-implemented method comprising:
collecting, by a computer, data from an Internet of Things (IoT) sensor system onboard a vehicle;
analyzing, utilizing a set of machine learning models trained on historical and real- time data, the collected data to assess driving behavior of a user of the vehicle combined with identification of an issue with a subsystem of the vehicle;
determining, by the computer, a customized maintenance schedule for the vehicle that takes into account the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle;
predicting, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle;
adjusting an operational parameter of the subsystem to maintain safe operating conditions and limiting operation of the vehicle to reduce operational stress on the subsystem;
scheduling, by the computer, a maintenance appointment with a vehicle repair shop computer, wherein the maintenance appointment corresponds to the identification of the issue with the subsystem of the vehicle at a date, time, and location based on availability of the user of the vehicle and a selected vehicle repair shop;
sending, by the computer, a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle;
collecting, by the computer, user feedback regarding the maintenance appointment;
and updating the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time and provide proactive detection by addressing maintenance issues to prevent vehicle breakdown and accidents caused by failure of one or more vehicle subsystems.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of collecting…, sending…, collecting… and updating… the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform data gathering, displaying, sending and receiving steps. In particular, the receiving and communicating steps are recited at a high level of generality (i.e. as a general means of receiving information and performing communications for use in the next steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The device(s) and processor(s) are recited at a high level of generality and merely automates the steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impost any meaningful limits on practicing the abstract idea. It is noted that “updating the machine learning models according to user feedback to increase predictive accuracy of the machine learning models” recites abstract ideas under machine learning (mathematics) and improvements to an abstract idea is still an abstract idea and cannot be considered additional limitations. Additionally, “taking preventative action…” and adjusting an operational parameter…” are not being considered as additional limitations since 35 U.S.C 112(a) and 112(b) rejections stand against those limitations.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claims 1, 8 and 14 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps collecting…, taking preventative action…, and scheduling… amounts to nothing more than insignificant extra-solution activities that merely use a computer (processor) to perform data gathering, displaying, sending and receiving steps. In particular, the receiving and communicating steps are recited at a high level of generality (i.e. as a general means of receiving information and performing communications for use in the next steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The device(s) and processor(s) are recited at a high level of generality and merely automates the steps.
Dependent Claims
Dependent claims 2-7, 9-13 and 15-20, do not recite any further limitations that causes the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-7, 9-13 and 15-20 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 8 and 14.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Roddy, Nicholas E. et al. (US 20030055666 A1) in view of GARDINER; DIARMUID et al. (US 20220165104 A1) and ABRAMOV; Erez (US 20210150827 A1).
Regarding Claim 1, Roddy teaches, A computer-implemented method for dynamic vehicle maintenance management (manage a fleet of mobile assets to avoid unexpected equipment failures and to accomplish maintenance and repair activities, see at least, ¶24-25, Roddy), the computer-implemented method comprising: collecting, by a computer, data from an Internet of Things (IoT) sensor system onboard a vehicle (Fig. 1 depicts mobile assets 12 or vehicle 26 are equipped with a plurality of sensors for monitoring a plurality of operating parameters of the remote asset, see at least, ¶24-25, Roddy); analyzing, utilizing historical and real- time data, the collected data to assess driving behavior of a user of the vehicle combined with identification of an issue with a subsystem of the vehicle (the method allows collecting historical and current usage data indicative of usage of the mobile asset which is processed relative to historical data to generate a prediction of failure in the mobile asset or subsystem of the vehicle, see at least, ¶6-8 and 26, Roddy); determining, by the computer, a customized maintenance schedule for the vehicle that takes into account the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (the diagnostic tools may use relative utilization benchmarking metrics as a factor processed by the tool in order to more accurately capture the underlying causes that may result in malfunctions and can be used to adjust the repair weight normally provided by the tool and adjust recommendation for repair based on the level of use of the asset, see at least, ¶24, 35-39 and 41, Roddy); predicting, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (on-board sensors on the third locomotive identify the degraded cooling condition and data related to the degraded condition is immediately downloaded 40 to the data center 18 to update the database 38 and computers at the datacenter may analyze the data 48 and identify that the anomaly exists 58, predicting equipment failure, see at least, ¶35-39, Roddy); taking preventative action, by the computer, to avoid the potential subsystem failure, wherein the preventative action comprises at least one of: adjusting an operational parameter of the subsystem to maintain safe operating conditions and limiting operation of the vehicle to reduce operational stress on the subsystem (truck tire wear reader records tire wear and inflation, and in the case of inadequate inflation or excessive tire wear, the diagnostic routine would provide real time corrective actions to the operator and possibly avoid a road failure, see at least, ¶93, Roddy); scheduling, by the computer, a maintenance appointment with a vehicle repair shop computer, wherein the maintenance appointment corresponds to the identification of the issue with the subsystem of the vehicle at a date, time, and location based on availability of the user of the vehicle and a selected vehicle repair shop (see at least, ¶86-87, Roddy); sending, by the computer, a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle via a network (a notification regarding the maintenance appointment is sent to the service shop, see at least, ¶86-87, Roddy); collecting, by the computer, user feedback regarding the maintenance appointment (results of inspection and maintenance visit are used to update the database 39 for the particular truck, see at least, ¶29, Roddy); and updating the performance reports to increase predictive accuracy over time and provide proactive detection by addressing maintenance issues to prevent vehicle breakdown and accidents caused by failure of one or more vehicle subsystems (performance reports 50 regarding each of the individual assets and statistical data 52 may be calculated to aid in the analysis of the operating parameters of the fleet, see at least, ¶29, Roddy).
Roddy does not explicitly teach, utilizing a set of machine learning models trained on historical and real- time data, sending, by the computer, a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle shop ; and updating the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time.
Gardiner, directed to predictive automotive servicing, teaches utilizing a set of machine learning models trained on historical and real- time data (machine learning models will be applied to predicting for teach type of repair using real-time and historical data, see at least, 122-139, Gardiner), sending, by the computer, a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle shop (customer confirming appointment date and time of proposed service schedules, see at least, ¶206-235, Gardiner);
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy’s method of scheduling vehicle maintenance to incorporate the teachings of Gardiner which teaches utilizing a set of machine learning models trained on historical and real- time data, sending, by the computer, a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle shop since they are both related to maintaining optimal performance of vehicles and incorporation of the teachings of Gardiner would increase customization of maintenance scheduling in accordance with driving habits of the driver.
Abramov, directed to a method for condition monitoring a vehicle and for alerting of irregularities/defects, teaches and updating the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time (AI model identify exceptional event based on human feedback, see at least, ¶70-71, Abramov).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy and Gardiner’s method of scheduling vehicle maintenance to incorporate the teachings of Abramov which teaches updating the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time since they are both related to maintaining optimal performance of vehicles and incorporation of the teachings of Abramov would improve response time to equipment defects/failure and vehicle breakdown [¶49].
Regarding Claim 8, Roddy teaches, A computer system for dynamic vehicle maintenance management, the computer system comprising: a communication fabric (Fig. 1 depicts a communications network, see at least, ¶10, Roddy); a storage device connected to the communication fabric, wherein the storage device stores program instructions; and a processor connected to the communication fabric, wherein the processor executes the program instructions to (Fig. 1 depicts a communications network and a computer containing a storage device stores instructions executed by a computer, see at least, ¶10 and 139, Roddy): collect data from an Internet of Things (IoT) sensor system onboard a vehicle (Fig. 1 depicts mobile assets 12 or vehicle 26 are equipped with a plurality of sensors for monitoring a plurality of operating parameters of the remote asset, see at least, ¶24-25, Roddy); analyze, by historical and real-time data, the collected data to assess driving behavior of a user of the vehicle combined with identification of an issue with a subsystem of the vehicle (the method allows collecting historical and current usage data indicative of usage of the mobile asset which is processed relative to historical data to generate a prediction of failure in the mobile asset or subsystem of the vehicle, see at least, ¶6-8 and 26, Roddy); determine a customized maintenance schedule for the vehicle that takes into account the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (the diagnostic tools may use relative utilization benchmarking metrics as a factor processed by the tool in order to more accurately capture the underlying causes that may result in malfunctions and can be used to adjust the repair weight normally provided by the tool and adjust recommendation for repair based on the level of use of the asset, see at least, ¶24, 35-39 and 41, Roddy); predict a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (on-board sensors on the third locomotive identify the degraded cooling condition and data related to the degraded condition is immediately downloaded 40 to the data center 18 to update the database 38 and computers at the datacenter may analyze the data 48 and identify that the anomaly exists 58, predicting equipment failure, see at least, ¶35-39, Roddy); take preventative action to avoid the potential subsystem failure, wherein the preventative action comprises at least one of: adjust an operational parameter of the subsystem to maintain safe operating conditions (truck tire wear reader records tire wear and inflation, and in the case of inadequate inflation or excessive tire wear, the diagnostic routine would provide real time corrective actions to the operator and possibly avoid a road failure, see at least, ¶93, Roddy) and schedule a maintenance appointment with a vehicle repair shop computer, wherein the maintenance appointment corresponds to the identification of the issue with the subsystem of the vehicle at a date, time, and location based on availability of the user of the vehicle and a selected vehicle repair shop (see at least, ¶86-87, Roddy); send a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle via a network (a notification regarding the maintenance appointment is sent to the service shop, see at least, ¶86-87, Roddy); collect user feedback regarding the maintenance appointment (results of inspection and maintenance visit are used to update the database 39 for the particular truck, see at least, ¶29, Roddy); and update performance reports according to increase predictive accuracy over time and provide proactive detection by addressing maintenance issues to prevent vehicle breakdown and accidents caused by failure of one or more vehicle subsystems (performance reports 50 regarding each of the individual assets and statistical data 52 may be calculated to aid in the analysis of the operating parameters of the fleet, see at least, ¶29, Roddy).
Roddy does not explicitly teach, analyze, by a set of machine learning models trained on historical and real-time data, predict, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle, send a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle; and update the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time.
Gardiner, directed to predictive automotive servicing, teaches analyze, by a set of machine learning models trained on historical and real-time data, predict, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (machine learning models will be applied to predicting for teach type of repair using real-time and historical data, see at least, 122-139, Gardiner), send a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle (customer confirming appointment date and time of proposed service schedules, see at least, ¶206-235, Gardiner);
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy’s method of scheduling vehicle maintenance to incorporate the teachings of Gardiner which teaches analyze, by a set of machine learning models trained on historical and real-time data, predict, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle, send a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle since they are both related to maintaining optimal performance of vehicles and incorporation of the teachings of Gardiner would increase customization of maintenance scheduling in accordance with driving habits of the driver.
Abramov, directed to a method for condition monitoring a vehicle and for alerting of irregularities/defects, teaches and update the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time (AI model identify exceptional event based on human feedback, see at least, ¶70-71, Abramov).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy and Gardiner’s method of scheduling vehicle maintenance to incorporate the teachings of Abramov which teaches and update the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time since they are both related to maintaining optimal performance of vehicles and incorporation of the teachings of Abramov would improve response time to equipment defects/failure and vehicle breakdown [¶49].
Regarding Claim 14, Roddy teaches, a computer program product for dynamic vehicle maintenance management, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to (Fig. 1 depicts a communications network and a computer containing a storage device stores instructions executed by a computer, see at least, ¶10 and 139, Roddy): collect data from an Internet of Things (IoT) sensor system onboard a vehicle (Fig. 1 depicts mobile assets 12 or vehicle 26 are equipped with a plurality of sensors for monitoring a plurality of operating parameters of the remote asset, see at least, ¶24-25, Roddy); analyze historical and real-time data, the collected data to assess driving behavior of a user of the vehicle combined with identification of an issue with a subsystem of the vehicle (the method allows collecting historical and current usage data indicative of usage of the mobile asset which is processed relative to historical data to generate a prediction of failure in the mobile asset or subsystem of the vehicle, see at least, ¶6-8 and 26, Roddy); determine a customized maintenance schedule for the vehicle that takes into account the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (the diagnostic tools may use relative utilization benchmarking metrics as a factor processed by the tool in order to more accurately capture the underlying causes that may result in malfunctions and can be used to adjust the repair weight normally provided by the tool and adjust recommendation for repair based on the level of use of the asset, see at least, ¶24, 35-39 and 41, Roddy); predict a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (on-board sensors on the third locomotive identify the degraded cooling condition and data related to the degraded condition is immediately downloaded 40 to the data center 18 to update the database 38 and computers at the datacenter may analyze the data 48 and identify that the anomaly exists 58, predicting equipment failure, see at least, ¶35-39, Roddy); take preventative action to avoid the potential subsystem failure, wherein the preventative action comprises at least one of: adjust an operational parameter of the subsystem to maintain safe operating conditions and alert the user of the vehicle to take immediate corrective action; limit operation of the vehicle to reduce operational stress on the subsystem (truck tire wear reader records tire wear and inflation, and in the case of inadequate inflation or excessive tire wear, the diagnostic routine would provide real time corrective actions to the operator and possibly avoid a road failure, see at least, ¶93, Roddy); schedule a maintenance appointment with a vehicle repair shop computer, wherein the maintenance appointment corresponds to the identification of the issue with the subsystem of the vehicle at a date, time, and location based on availability of the user of the vehicle and a selected vehicle repair shop (see at least, ¶86-87, Roddy); send a notification regarding the maintenance appointment corresponding to the identification of the issue with the subsystem of the vehicle to the user of the vehicle via a network (a notification regarding the maintenance appointment is sent to the service shop, see at least, ¶86-87, Roddy); collect user feedback regarding the maintenance appointment (results of inspection and maintenance visit are used to update the database 39 for the particular truck, see at least, ¶29, Roddy); and update performance reports to increase predictive accuracy over time and provide proactive detection by addressing maintenance issues to prevent vehicle breakdown and accidents caused by failure of one or more vehicle subsystems (performance reports 50 regarding each of the individual assets and statistical data 52 may be calculated to aid in the analysis of the operating parameters of the fleet, see at least, ¶29, Roddy).
Roddy does not explicitly teach analyze, by a set of machine learning models trained on historical and real-time data, predict, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle; and update the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time.
Gardiner, directed to predictive automotive servicing, teaches analyze, by a set of machine learning models trained on historical and real-time data, predict, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle (machine learning models will be applied to predicting for teach type of repair using real-time and historical data, see at least, 122-139, Gardiner), the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle (customer confirming appointment date and time of proposed service schedules, see at least, ¶206-235, Gardiner).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy’s method of scheduling vehicle maintenance to incorporate the teachings of Gardiner which teaches analyze, by a set of machine learning models trained on historical and real-time data, predict, utilizing the set of machine learning models, a potential subsystem failure from the assessed driving behavior of the user and the identification of the issue with the subsystem of the vehicle, the notification including at least the date, time, and location of the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle since they are both related to maintaining optimal performance of vehicles and incorporation of the teachings of Gardiner would increase customization of maintenance scheduling in accordance with driving habits of the driver.
Abramov, directed to a method for condition monitoring a vehicle and for alerting of irregularities/defects, teaches and update the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time (AI model identify exceptional event based on human feedback, see at least, ¶70-71, Abramov).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy and Gardiner’s method of scheduling vehicle maintenance to incorporate the teachings of Abramov which teaches and update the machine learning models according to user feedback to increase predictive accuracy of the machine learning models over time since they are both related to maintaining optimal performance of vehicles and incorporation of the teachings of Abramov would improve response time to equipment defects/failure and vehicle breakdown [¶49].
Claims 2, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Roddy, Nicholas E. et al. (US 20030055666 A1) in view of GARDINER; DIARMUID et al. (US 20220165104 A1) and ABRAMOV; Erez (US 20210150827 A1) as applied to claims 1, 8 and 14 and further in view of Bachant; Thomas et al. (US 20200364950 A1).
Regarding Claims 2, 9 and 15, Roddy in view of Gardiner and Abramov teaches, the computer-implemented method of claim 1, further comprising (re-claim 2), The computer system of claim 8, wherein the processor further executes the program instructions to: (re-claim 9), and the computer program product of claim 14, wherein the program instructions further cause the computer to: (re-claim 15)
Roddy in view of Gardiner and Abramov does not explicitly teach, determining, by the computer, whether the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle is needed prior to the vehicle arriving at a destination; and scheduling, by the computer, the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle at a nearest available vehicle repair shop prior to the vehicle arriving at the destination in response to the computer determining that the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle is needed prior to the vehicle arriving at the destination.
Bachant, directed to systems and methods for self-maintenance of an autonomous rideshare vehicle teaches, determining, by the computer, whether the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle is needed prior to the vehicle arriving at a destination (Fig. 4A block 310, if the diagnostics data determines the issue is a high criticality level, the ride comes to a stop and passengers are picked up by a backup service and the vehicle does not finish the current trip to the planned destination, see at least, ¶49, Fig. 4A, Bachant); and
scheduling, by the computer, the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle at a nearest available vehicle repair shop prior to the vehicle arriving at the destination in response to the computer determining that the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle is needed prior to the vehicle arriving at the destination (Fig. 4A block 316, the system calls a tow truck to take the vehicle to the nearest maintenance facility and a work order for maintenance is scheduled in block 318 prior to the vehicle arriving at the destination, see at least, ¶49, Fig. 4A, Bachant).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy in view of Gardiner and Abramov to incorporate the teachings of Bachant which teaches determining, by the computer, whether the maintenance corresponding to the issue detected in the subsystem of the vehicle is needed prior to the vehicle arriving at a destination; and scheduling, by the computer, the maintenance corresponding to the issue detected in the subsystem of the vehicle at a nearest available vehicle repair shop prior to the vehicle arriving at the destination in response to the computer determining that the maintenance corresponding to the issue detected in the subsystem of the vehicle is needed prior to the vehicle arriving at the destination since they are both related to maintaining optimal performance of vehicles and incorporation of the teachings of Bachant would increase the reliability and convenience of the overall system by reacting to an emergency situation where the vehicle cannot safely drive to the maintenance facility.
Claims 3, 6-7, 10, 13, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Roddy, Nicholas E. et al. (US 20030055666 A1) in view of GARDINER; DIARMUID et al. (US 20220165104 A1) and ABRAMOV; Erez (US 20210150827 A1) as applied to claims 1, 8 and 14 and further in view of Penilla; Angel A. et al. (US 20210287184 A1).
Regarding Claims 3, 10 and 16 Roddy in view of Gardiner and Abramov teaches, the computer-implemented method of claim 1, further comprising: (re-claim 3), The computer system of claim 8, wherein the processor further executes the program instructions to: (re-claim 10), and the computer program product of claim 14, wherein the program instructions further cause the computer to: (re-claim 16).
Roddy in view of Gardiner and Abramov does not explicitly teach receiving, by the computer, a confirmation regarding the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle from the user of the vehicle via the network.
Penilla, directed to systems and methods for managing user profiles for vehicles and exchange of information with cloud-based processing systems, and diagnosing vehicle conditions, and providing recommendations teaches, receiving, by the computer, a confirmation regarding the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle from the user of the vehicle via the network (Fig. 24, block 3016, user elects to create a job (confirmation regarding the maintenance appointment) for the required service (issue detected in the subsystem of the vehicle) via the client/server VSW network , see at least, ¶229, Fig. 24, Penilla)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy in view of Gardiner and Abramov to incorporate the teachings of Penilla which teaches receiving, by the computer, a confirmation regarding the maintenance appointment corresponding to the issue detected in the subsystem of the vehicle from the user of the vehicle via the network since they are both related to vehicle maintenance systems and incorporation of the teachings of Penilla would increase the safety of operating the vehicle informing the driver of the condition of the vehicle if preventative maintenance is required to keep the vehicle operating in good condition.
Regarding Claims 6, 13 and 19, Roddy in view of Gardiner and Abramov teaches, the computer-implemented method of claim 1, further comprising: (re-claim 6), the computer system of claim 8, wherein the processor further executes the program instructions to: (claim 13) and the computer program product of claim 14, wherein the program instructions further cause the computer to: (re-claim 19)
Roddy in view of Gardiner and Abramov does not explicitly teach receiving, by the computer, an input to establish a wireless connection with the vehicle via the network from the user of the vehicle; establishing, by the computer, the wireless connection with the vehicle via the network in response to receiving the input; receiving, by the computer, a registration of the vehicle for a vehicle maintenance management service provided by the computer from the user of the vehicle via the network; and generating, by the computer, a record corresponding to the vehicle in a vehicle maintenance data structure of the vehicle maintenance management service in response to receiving the registration of the vehicle.
Penilla, directed to systems and methods for managing user profiles for vehicles and exchange of information with cloud-based processing systems, and diagnosing vehicle conditions, and providing recommendations teaches, receiving, by the computer, an input to establish a wireless connection with the vehicle via the network from the user of the vehicle (user input triggers vehicle client application connection with the server, see at least, ¶28, Penilla);
establishing, by the computer, the wireless connection with the vehicle via the network in response to receiving the input (establishing a wireless connection with the vehicle over a network, see at least, ¶10, Penilla);
receiving, by the computer, a registration of the vehicle for a vehicle maintenance management service provided by the computer from the user of the vehicle via the network (Fig. 25 depicts a flow of events for managing vehicle repairs and generating a job on VSW/Server application (block 3116) for service providers to bid on, see at least, ¶233, Fig.23, Penilla);
and generating, by the computer, a record corresponding to the vehicle in a vehicle maintenance data structure of the vehicle maintenance management service in response to receiving the registration of the vehicle (Fig. 25 depicts the job generated contains associated data 3118 which is interpreted as the vehicle maintenance data structure in response to creating a job or registration of the vehicle, see at least, ¶233-234, Fig. 25, Penilla) (Fig. 18A depicts examples of data types such as a job 2402, see at least, ¶51, Fig. 25, Penilla)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy in view of Gardiner and Abramov to incorporate the teachings of Penilla which teaches teach receiving, by the computer, an input to establish a wireless connection with the vehicle via the network from the user of the vehicle; establishing, by the computer, the wireless connection with the vehicle via the network in response to receiving the input; receiving, by the computer, a registration of the vehicle for a vehicle maintenance management service provided by the computer from the user of the vehicle via the network; and generating, by the computer, a record corresponding to the vehicle in a vehicle maintenance data structure of the vehicle maintenance management service in response to receiving the registration of the vehicle, since they are both related to vehicle maintenance systems and incorporation of the teachings of Penilla would increase the safety of operating the vehicle informing the driver of the condition of the vehicle if preventative maintenance is required to keep the vehicle operating in good condition.
Regarding Claims 7 and 20, Roddy in view of Gardiner, Abramov and Penilla teaches, the computer-implemented method of claim 6, further comprising: (re-claim 7) and the computer program product of claim 19, wherein the program instructions further cause the computer to: (re-claim 20).
Roddy in view of Gardiner and Abramov does not explicitly teach enabling, by the computer, the user to customize settings of the vehicle maintenance management service according to user preference for the vehicle.
Penilla, directed to systems and methods for managing user profiles for vehicles and exchange of information with cloud-based processing systems, and diagnosing vehicle conditions, and providing recommendations teaches, enabling, by the computer, the user to customize settings of the vehicle maintenance management service according to user preference for the vehicle (notifications or alerts sent to the user are customized for the user based on learned patterns of inputs made by the user, and user can customize settings, and configurations defined by the user), see at least, ¶26, 70, Penilla).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Roddy in view of Gardiner and Abramov to incorporate the teachings of Penilla which teaches enabling, by the computer, the user to customize settings of the vehicle maintenance management service according to user preference for the vehicle since they are both related to vehicle maintenance systems and incorporation of the teachings of Penilla would increase the safety of operating the vehicle informing the driver of the condition of the vehicle if preventative maintenance is required to keep the vehicle operating in good condition.
Claims 4-5, 11-12, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Roddy, Nicholas E. et al. (US 20030055666 A1) in view of GARDINER; DIARMUID et al. (US 20220165104 A1) and ABRAMOV; Erez (US 20210150827 A1) as applied to claims 1, 8 and 14 and further in view of LIU; Yimin et al. (US 20210215491 A1).
Regarding Claims 4, 11 and 17, Roddy in view of Gardiner and Abramov teaches, he computer-implemented method of claim 1, further comprising: (re-claim 3), The computer system of claim 8, wherein the processor further executes the program instructions to: (re-claim 11), and the computer program product of claim 14, wherein the program instructions further cause the computer to: (re-claim 17)
Roddy in view of Gardiner and Abramov does not explicitly teach collecting, by the computer, the data regarding performance of each subsystem from a plurality of subsystems from the IoT sensor system onboard the vehicle via the network.
Liu, directed to a vehicle sharing system teaches, collecting, by the computer, the data regarding performance of each subsystem from a plurality of subsystems from the IoT sensor system onboard the vehicle via the network. (Fig.9 depicts a block diagram depicting an internet of things implementation of collecting data from vehicle-related sensors to detect water content of different fluids such as engine oil, transmission oil, brake fluid, or power steering fluid. The sensor data is collected to a vehicle embedded modem 932 which is the IoT sensor system and sends the data to the mobile device for analysis and processing for detecting wear and tear of the subsystems displayed in item 928, see at least [¶81, Fig. 9, Liu], and system or component wear and tear can be detected and priced accordingly by include using sensor data to determine road condition in combination with driving behavior, see at least, [¶65, Liu]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified the invention of Roddy in view of Gardiner and Abramov to incorporate the teachings of Liu which teaches collecting, by the computer, the data regarding the performance of each subsystem of the plurality of subsystems corresponding to the vehicle and the driving behavior of the user of the vehicle from the IoT sensor system onboard the vehicle via the network since they are both related to maintenance of vehicle systems and incorporation of the teachings of Liu would increase utility of the overall system since detecting the wear and tear of essential systems of the vehicle could anticipate maintenance costs associated with each ride and charge the appropriate amount to make up for the cost of wear and tear on the vehicle.
Regarding Claims 5, 12 and 18, Roddy in view of Gardiner and Abramov and Liu teach, the computer-implemented method of claim 4 (re-claim 5), the computer system of claim 11 (re-claim 12) and the computer program product of claim 17 (re-claim 18)
Liu, directed to a vehicle sharing system further teaches wherein each subsystem of the plurality of subsystems corresponding to the vehicle includes a corresponding set of IoT sensors of the IoT sensor system (Fig.9 depicts a block diagram depicting an internet of things implementation of collecting data from vehicle-related sensors to detect water content of different fluids such as engine oil, transmission oil, brake fluid, or power steering fluid. The sensor data is collected to a vehicle embedded modem 932 which is the IoT sensor system and sends the data to the mobile device for analysis and processing for detecting wear and tear of the respective subsystems displayed in item 928, see at least [¶81, Fig. 9, Liu]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified the invention of Roddy in view of Gardiner and Abramov and Liu to further incorporate the teachings of Liu which teaches wherein each subsystem of the plurality of subsystems corresponding to the vehicle includes a corresponding set of IoT sensors of the IoT sensor system since they are both related to maintenance of vehicle systems and incorporation of the teachings of Liu would increase the utility of the overall system since detecting the wear and tear of essential subsystems of the vehicle could anticipate maintenance costs associated with each ride and charge the appropriate amount to make up for the cost of wear and tear on the vehicle.
Conclusion
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/IRENE C KHUU/
Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664